Brief definitions of several terms used herein follow, which may be helpful to certain readers. Such definitions, although brief, will help those skilled in the relevant art to more fully appreciate aspects of the invention based on the detailed description provided herein. Such definitions are further defined by the description of the invention as a whole and not simply by such definitions.
Media AssetA specific impression, airing, or advertising event. Forinstanceexample, a media asset may be CNN-Monday throughFriday.An Asset instance of this asset may be CNN-Tuesday-8:05 pm-AC360.Media assetAdvertising media that can be published for purposes ofadvertising. Examples include television airing, radiospot, newspaper spot, internet publisher page. Forexample, a television media asset may comprise somecombination of station-geography-program-day-hourssuch as WXGN-Florida-SixO'ClockNews-Monday-6 pm,or may be a more general set such as FOX-Mondaythrough Friday.AssetSame as media assetMediaSame as media assetStationSame as media assetStation-Same as media assetProgramStation-Same as media assetProgram-Day-HourProductSomething that is being sold by an advertiser. Theproduct and advertisement are used interchangeably -each product is assumed to have one or moreadvertisements that can be aired on television media.The contents of the advertisement are considered partof the product for the present system in order to simplifythe description.AdvertisementSame as ProductSpotSame as media asset instance. Often “spot” is a termused for TV advertising which is one of theembodiments.PlacementSame as media asset instance. “Placement” is often usedfor TV advertising which is one of the embodiments.
Television is an incredibly successful medium. The average American spent almost 37 hours a week watching television in 2009—this is over twice time spent online (Leaders, (2010), In Praise of Television: The great survivor, The Economist, April 2010). Yet at the same time television presents formidable challenges in measuring and optimizing television advertising. Customers almost always view ads on TV and convert through other channels including web and retail stores. This is a fundamental problem. Kokernak (2010) suggests that “until we can develop cross-platform metrics, additional new business models for television will be nearly impossible to establish.” Kokernak, M. (2010), What's Television's Next Business Model? Media Post Daily News, Wednesday, Mar. 17, 2010 http://www.mediapost.com/publications/?fa=Articles.showArticle&art_aid=124424
A. Conversion Tracking
Introductions to online conversion tracking systems can be found at Google adWords Help, (2007a), What is Conversion Optimizer and How Does it Work? http://adwords.google.com/support/aw/bin/answer.py?hl=en&answer=60150, and Kitts, B. (2009), adCenter Announces new Conversion Tracking Options, adCenter Blog, Mar. 16, 2009. This ability to track has allowed for the development of automated systems for bidding and managing Cost-per-Action CPA goals (Kitts, B., LeBlanc, B. (2004), Optimal Bidding on Keyword Auctions, Electronic Markets—The International Journal of Electronic Commerce and Business Media, Vol. 14, No. 3; Google, (2008), CPA Performance Trends on the Google Content Network, Google Inc., http://www.google.com/ads/research/gcnwhitepaper/whitepaper.pdf; Google, 2007a; Google adWords Blog, (2007b), New PPA Bidding Product Available, September 2007).
The most common industry approach for understanding who is viewing the advertisements is the use of viewer panels. These are volunteer users who allow their activities to be monitored. The Nielsen panel contains 25,000 users (out of approximately 114.5 million television households) and so the Nielsen sample is less than 0.022% of population. This small sample size creates a significant challenge for some products which have smaller sales—or where the audience being sought after is much smaller, such as elite credit card customers. The panel may simply not have enough users who buy the product to make reliable inferences about television spot performance—or it may have some information for broadcast channels, but lacking information for local stations and cable.
Other techniques for tracking TV include embedding special offers, phone numbers or vanity URLs into the advertisement. When a customer calls in to order, the company can uniquely identify the airing which the customer viewed because they use the phone number, URL, or redeem the offer. Linking keys have limited applications since only a small fraction of the population will ultimately use the embedded key—often customers convert without these tracking devices.
B. Credit Assignment
After marketing events have been tracked, the next problem is to determine which of the tracked events “caused” a customer to convert. Although statistics technically may not be able to answer the ultimate question of causality, approaches such as Structural Equation modeling are typically used for inferring relatedness of advertising to customer sales. Algorithms such as TD-Lambda (Sutton and Barto, 1998) maintain statistics on success likelihoods conditional upon events, and assign credit backwards in time after a positive event such as a conversion. Sutton and Barto (1998), Reinforcement learning: An introduction, MIT Press http://webdocs.cs.ualberta.ca/˜sutton/book/the-book.html. These statistics can then be used to infer precedents with the greatest chance of bringing about a conversion. “Engagement mapping” which has been proposed by Atlas and aims to assign credit to multiple preceding events, can be seen as an application of TD-Lambda style credit assignment. Last ad click conversions can also be considered a subset of reinforcement learning theory.
C. Cross-Modal Conversions
A variety of studies have begun to look into the problem of multi-modal conversion tracking, and specifically have called into question web-based conversion tracking numbers. Brooks et al. (2009) noted that 71% of conversions in clients of the Atlas system were from navigational queries. Brooks, N. (2009), Paying for Navigational Search, Atlas Digital Media Insights, http://www.atlassolutions.com/uploadedFiles/Atlas/Atlas_Institute/Published_Content/dmi-NavigationalSearch.pdf. Rimm-Kaufman (2007) noted that 50% of clicks may be on brandname keywords. Rimm-Kaufman, A. (2007), PPC And Your Good Name: Sales From Brand Searches Aren't Incremental May 27, 2007 http://searchengineland.com/ppc-and-your-good-name-sales-from-brand-searches-arent-incremental-10825. This is suggestive that customers already know about the product and so had essentially been acquired through a different marketing event or offer. Chandler-Pepelnjak (2009) also noted that assigning credit to the last click ignores all other channels that may be bringing about the conversion. Chandler-Pepelnjak, J. (2009), Measuring ROI beyond the Last Ad, Atlas Digital Marketing Insights, http://www.atlassolutions.com/uploadedFiles/Atlas/Atlas_Institute/Published_Content/dmi-NavigationalSearch.pdf.